Feature selection algorithms for Malaysian dengue outbreak detection model

Dengue fever is considered as one of the most common mosquito borne diseases worldwide. Dengue outbreak detection can be very useful in terms of practical efforts to overcome the rapid spread of the disease by providing the knowledge to predict the next outbreak occurrence. Many studies have been conducted to model and predict dengue outbreak using different data mining techniques. This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). Based on the selected features, three predictive modeling techniques (J48, DTNB and Naive Bayes) were applied for dengue outbreak detection. The dataset used in this research was obtained from the Public Health Department, Seremban, Negeri Sembilan, Malaysia. The experimental results showed that the predictive accuracy was improved by applying feature selection process before the predictive modeling process. The study also showed the set of features to represent dengue outbreak detection for Malaysian health agencies

A Bahasa Malaysia interactive book app as a speech-language therapy tool for children with language delay

Interactive story books (ISB) mobile application is defined as an app for children to practice their speech, language and literacy skills. Information has shown that ISB can be used to support education and learning for individuals with language delay. In general, visual and auditory artifacts are included in this kind of app which makes the app an incredible aid to facilitate speech and language skills. Considering the situation in Malaysia, many of the developed ISB suffers from a lack of practical experience with children, especially for the children who have language delay. The evidence can be seen from the lack of apps for the children whose first language is Bahasa Malaysia. Malaysian children with language delay and their parents are the subjects in this research. The children and their parents were asked to answer a survey about the ISB requirement. The survey focused on collecting the requirement for the ISB targeted for children with language delay. The survey data were analyzed to get the requirements for the app. Based on the requirements, the ISB can be developed using the Android platform. The main finding of the study is the practical requirements of ISB in Bahasa Malaysia for children with language delay in collaboration with Speech and Language Pathologists (SLPs), parents and children

Knowledge lattice approach for web courseware authoring

In this paper, we present the application of knowledge lattice approach in a Web-based courseware authoringsupport system.Knowledge lattice is widely known as ontology in Semantic Web environment.Onlotogy is a formal and declarative representation for knowledge. Knowledge is represented in the form of chunks in several layers. This approach is being used to develop Instructional Management Support System (IMSS).IMSS aims to support students, teachers, courseware developers, administrators, and parents. For example, it supports teachers by managing tasks such as monitoring progress and presenting subject content in a Web-based learning environment. Knowledge lattice is applied as the main interface to navigate the system modules. The lattice is used to represent the subject domain by linking the basic knowledge chunks to more advanced knowledge chunks.To further explain this approach, we define a set of functions related to each knowledge chunks

Towards gene network estimation with structure learning

Gene network is a representation of gene interactions. A gene usually collaborates with other genes in order to function. Understanding these interactions is a crucial step towards understanding how our body functions. Bayesian Network is a technique that was initially used in Expert System to represent expert knowledge. Since the pioneer work of Friedman et al. that applied this technique to analyse gene expression data, other researchers have enhanced the technique further. This research concentrates on enhancing Bayesian Network technique fro learning gene network. In order to get better results, Bayesian technique will be used with prior knowledge. The tool that is used to learn the gene network is PNL(Probabilistic Network Library). Early results show that PNL can be used to recover gene network for 3 subnetworks for S.Cerevisiae. These 3 subnetworks has been learned using PNL with varying success. The next step in this research is to learn the gene network from the dataset of 800 genes. The knowledge that will be gained will be used to produce a better approach to learning gene network using Bayesian network techniqu

Gene network inference using biological homogeneity index based-clustering and constraint-based searching

Gene network inference involves exploration of an exponential search space. Initially, network inference utilizes microarray data as a single data source. However, due to microarray data limitations, other biological data is combined with microarray data for network inference. Previous research has produced biological homogeneity measures based on functional annotations from Gene Ontology for various clustering algorithms. Biological homogeneity measures the ability of a clustering algorithm to produce biologically meaningful clusters. Biological Homogeneity Index (BHI) is measured for a range of fc values for fc-means clustering algorithm to find clusters which score the highest homogeneity index. Results are compared using whole dataset, fc-means clusters and fc-means clusters with BHI (fc-means /BHI) approaches. Experimental results have shown that the fc-means clusters produced statistically significant valid number of gene interactions compared to the whole dataset. In comparing the fc-means clusters and fc-means /BHI clusters, the fc-means /BHI clusters produces more valid number of gene interactions for all experiments. Statistical significance test results show that these improvements are too small to be statistically significant. Hence, biological enrichment scores are also used for evaluation. Enrichment scores for fc-means /BHI clusters are better than scores for fc-means clusters. This research employs the constraint-based search algorithm called Grow-Shrink algorithm (GS) in learning the best network structure. Experiments are performed to compare the performance for constraint-based search against scorebased approaches such as the Greedy Search (GRS) and Simulated Annealing (SA). Experimental results prove that GS performs better than GRS and SA in terms of valid interactions number. However, the improvements are too small to be statistically significant. The thesis concludes that using prior biological knowledge can help form biologically meaningful clusters. Using constraint-based search algorithm is also useful for improving the quality of gene network inference

Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling

Intrusion detection has received a lot of attention from many researchers, and various techniques have been used to identify intrusions or attacks against computers and networks. Data mining is a well-known artificial intelligence technique to build network intrusion detection systems. However, numerous data mining techniques have been successfully applied in this area to find intrusions hidden in large amounts of audit data through classification, clustering or association rule. Clustering is one of the promising techniques used in Anomaly Intrusion Detection (AID), especially when dealing with unknown patterns. This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART

Towards understanding bayesian network-based inferred gene interactions

It is hoped that this seminar will inculcate the right culture that is geared towards ensuring passion, dedication and commitment for research activities that will be good enough to provide support to the UTM theme “Innovative, Entrepreneurial and Global”. It is also hoped that the seminar will motivate students to participate in national and international conferences. It is anticipated that the students will appreciate the feedback from the various evaluators and get motivated with them